\captionsetup[table]{format=myappxtabcap}
\setcounter{table}{0}
\pdfbookmark[1]{Appendix Tables}{appx-tables}

```{=latex}
\tablepdf
    {data_processing} % label
    {Data Sources and Processing Steps} % title
    {tables_cropped.pdf} % path to PDF with cropped tables
    {3} % page number in PDF
%%%%% Notes %%%%%
    {This table provides an overview of the data series constructed in this paper and published at \url{tracker.opportunityinsights.org}. Further details are provided in Section \ref{sec:Data-Series} and in the appendices referenced in the table.}
    {}
```

```{=latex}
\tablepdf
    {sample_size} % label
    {Distributions of Sample Sizes} % title
    {tables_cropped.pdf} % path to PDF with cropped tables
    {4} % page number in PDF
%%%%% Notes %%%%%
    {This table presents the distribution of cell sizes for data series we construct from private sector sources. For Affinity Solutions, we report the population-weighted percentiles of the county-level distribution of the average daily total spending during January 4 to 31, 2019. For Womply, we report population-weighted percentiles of the county-level and ZIP-level distributions of weekly revenue in January 4 to 31, 2020. So, for example, the \$774,550 under the column “10th Percentile” means that the top 90\% of the population lives in counties with at least \$774,550 in average weekly small business revenue in the base period. For Lightcast, we report population-weighted percentiles of the county-level distribution of average weekly job posts in January 4 to 31, 2020. For Paychex-Intuit, we report population-weighted percentiles of the county-level distribution of employment in January 4 to 31, 2020. For Earnin, we report population-weighted percentiles of the county-level and ZIP-level distributions of employment in January 4 to 31, 2020. For Zearn, we report the average number of Zearn students and the share of Zearn students in the top 5; top 10; top 20; top 50; and top 100 counties by county population. We also report the share of the U.S. population in these counties for reference. Data sources: Affinity Solutions, Womply, Lightcast, Paychex, Intuit, Earnin, Zearn.}
    {}
```

```{=latex}
\tablepdf
    {industry_share} % label
    {Industry Employment Shares Across Datasets} % title
    {tables_cropped.pdf} % path to PDF with cropped tables
    {5} % page number in PDF
%%%%% Notes %%%%%
    {This table compares the industry composition (2-digit NAICS) of two payroll-based employment datasets to the Quarterly Census of Employment and Wages (QCEW), an administrative dataset covering the near-universe of firms in the United States. Each column displays the share of employees (in percentage terms) in the given dataset who work in the specified sector. Column (1) displays the industry composition of the QCEW in the first quarter of 2020. Column (2) replicates column (1) restricting to small establishments, defined as establishments with fewer than 50 employees. Column (3) shows the industry composition of Paychex-Intuit data in January 2020. To construct Column (3), we first separately calculate the number of employees in each 2-digit NAICS code in Paychex and Intuit as the number of worker-days in each 2-digit NAICS code. We then calculate combined Paychex-Intuit employment in each 2-digit NAICS code as a weighted sum of Paychex and Intuit, where the weights are undisclosed to meet privacy protection requirements. Column (4) displays the industry composition in the Earnin data in January 2020. Data sources: Paychex, Intuit, Earnin, QCEW.}
    {}
```
```{=latex}
\tablepdf
    {wage_oes} % label
    {Hourly Wage Rates By Industry Across Datasets} % title
    {tables_cropped.pdf} % path to PDF with cropped tables
    {6} % page number in PDF
%%%%% Notes %%%%%
    {This table compares mean wages in private sector datasets to mean wages in Occupational Employment Statistics (OES) data, within each two-digit NAICS code. Column (1) reports mean wages in each NAICS code in May 2019 OES data. We inflate these wages to 2020 dollars using the BLS Consumer Price Index. Column (2) reports mean wages in combined Paychex-Earnin data in January 2020. We first compute mean wages separately in Paychex and Earnin data as mean wages in January 2020, weighting by number of worker-days. In Paychex, wages are measured as pre-tax wages recorded by the employer. In Earnin, wages are post-tax wages recorded in payroll deposits. We then take a weighted mean of Paychex and Earnin wages within each industry, where the weights are not disclosed to meet business privacy requirements. The last row of Column (2) displays BLS mean wages, reweighted to match the 2-digit NAICS composition within the combined private sector dataset. Data sources: Paychex, Earnin, OES.}
    {}
```

```{=latex}
\tablepdf
    {spending_changes} % label
    {Consumer Spending on Debit and Credit Cards, by Income Quartile and Sector} % title
    {tables_cropped.pdf} % path to PDF with cropped tables
    {7} % page number in PDF
%%%%% Notes %%%%%
    {This table presents estimates of the changes in daily national consumer spending from the pre-pandemic baseline (measured as a daily average between January 6 to February 2, 2020) to April 14, 2020 (in Column 2), August 14, 2020 (in Column 3) and December 31, 2021 (in Column 4). We construct these estimates by combining statistics on total daily card spending in January 2020 (categories “Furnishings and durable household equipment”, “Recreational goods and vehicles”, “Other durable goods”, “Food and beverages purchased for off-premises consumption”, “Clothing and footwear”, “Gasoline and other energy goods”, “Other nondurable goods”, “Transportation services”, “Recreation services”, “Food services and accommodations”, “Financial services and insurance”, and “Other services” from NIPA Table 2.3.5) with our consumer spending series from Affinity Solutions. See Section \ref{subsec:Data-Spending} and Appendix \ref{sec:Appx-Affinity} for more details on the construction of the consumer spending series. We estimate total daily spending (top row in Column 1) by dividing the NIPA monthly estimate by 31. We estimate the levels in the remaining rows of Column 1 by multiplying the spending share estimated in the Affinity data in January 2020, by total daily spending. We estimate the changes in Columns 2-4 in each row by multiplying the level in Column 1 by the change in the relevant Affinity series. The parentheses in each row report the share of the national spending decline (in Columns 2-4) or of the national spending total (in Column 1) accounted for by each subset. Panel A disaggregates spending by income quartile, measured at the ZIP code level using median household income from the 2014-2018 ACS. Panel B disaggregates spending by sector; Panel C disaggregates in-person service spending by sub-sector (see Appendix \ref{subsec:Data-Affinity-Masking-and-Publication} for sector and sub-sector definitions). Data sources: Affinity Solutions, NIPA.}
    {}
```

```{=latex}
\tablepdf
    {policy_stimulus} % label
    {Effects of Stimulus Payments on Consumer Spending} % title
    {tables_cropped.pdf} % path to PDF with cropped tables
    {8} % page number in PDF
%%%%% Notes %%%%%
    {This table reports difference-in-differences estimates of the impacts of the three rounds of economic impact payments on consumer spending, separately for each ZIP income quartile. Panels A, B, and C respectively show estimates for the payments primarily made on April 15, 2020, January 4, 2021, and March 17, 2021. In Panels A and C, we use a window of 25 days before and after the stimulus payment date as the estimation sample. In Panel A, we exclude the partially treated date of April 14, 2020 and in Panel C, we exclude the partially treated dates of March 13 to 16, 2021. In Panel B, we use December 4 to 14, 2020 and January 4 to 19, 2021 as the estimation window; we exclude the intervening holiday period because of the high degree of volatility of spending during that period (Appendix Figure \ref{fig:variance_week}). We then regress daily consumer spending within the relevant window and the corresponding period starting in 2019 (after residualizing it on day-of-week fixed effects) on an indicator variable for the first five days of the post-period, an indicator variable for the rest of the post-period, and their interactions with an indicator for being in the treated group. In Panels A and C, we also adjust for a linear pre-trend in both the treatment and control series; we do not adjust for a linear pre-trend in Panel B due to the omission of the holiday period. The coefficients reported in columns 1 and 2 are those on the interaction of the two post-period indicators with the indicator for the treated year. Column 3 combines these two spending estimates to project the total percentage effect on spending for the first 31 days after the reform. Column 4 converts this combined percentage estimate into a total dollar estimate using base period daily average spending (see Appendix \ref{sec:Appx-Stimulus-Policy} for details) and column 5 rescales the dollar estimates to be per \$1,200 of stimulus. Robust standard errors are reported in parentheses. Data source: Affinity Solutions.}
    {} %footnote font size
```

```{=latex}
\tablepdf
    {spending_rent} % label
    {Association Between Changes in Consumer Spending and Workplace Rent by ZIP Code} % title
    {tables_cropped.pdf} % path to PDF with cropped tables
    {9} % page number in PDF
%%%%% Notes %%%%%
    {This table presents results from regressions of changes in low-income consumer spending in the first month of the pandemic on the median rent of the workplaces of those low wage workers.  We measure the dependent variable as the average value of our consumer spending index between March 25 and April 14, 2020 (see Section \ref{subsec:Data-Spending} and Appendix \ref{sec:Appx-Affinity} for details on the construction of this series).  Unlike our baseline spending series, we compute these data for this table at the ZIP code level.  We then construct the average workplace rent  using the Census' LODES data (to measure the workplace ZIP codes for low-wage workers residing in each ZIP code) and the median rent data for each ZIP code from the 2014-2018 ACS.  Our independent variable is the average of median workplace ZIP rents using the LODES workplace distribution as the weights for each residential ZIP code.  We then restrict the sample for the regression to the residential ZIP codes in the (population-weighted) bottom quartile of median income.  We scale the dependent and independent variables such that the coefficients represent the predicted change in percentage points for each \$1000 increase in rent; for instance, the coefficient of -{{coef_rent_work_apr_jan}} in Column (1) means that a \$1000 increase in average workplace median rent for low-wage workers residing a given low-income ZIP code is associated with a {{coef_rent_work_apr_jan}} percentage point reduction in consumer spending in that ZIP code. Column (2) replicates the specification in Column (1) including county fixed effects. Standard errors are clustered at the county level and reported in parentheses. Data sources: Affinity Solutions, Census LODES, ACS.}
    {}
```

```{=latex}
\tablepdf
    {zearn_demographic} % label
    {Demographic Characteristics of Zearn Users} % title
    {tables_cropped.pdf} % path to PDF with cropped tables
    {10} % page number in PDF
%%%%% Notes %%%%%
    {This table reports demographic characteristics for Zearn schools vs. the U.S. population. Panel A compares income characteristics of ZIP codes with Zearn coverage vs. all ZIP codes. We define Zearn to have coverage in a ZIP code if at least five students at schools in that ZIP code used Zearn between January 6 to February 7, 2020. Column 1 shows income characteristics of Zearn-covered ZIP codes. The first three rows in Panel A display the 25th, 50th, and 75th percentiles of ZIP-level median household income in Zearn-covered ZIP codes, as measured in the 2014-2018 ACS. The fourth and fifth rows of Panel A display the number of Zearn-covered ZIP codes, and the number of students using Zearn in those ZIP codes. Column 2 replicates Column 1 using all ZIP codes in the U.S. The fourth and fifth rows of Column 2 replicates Column 1 using all ZIP codes in the U.S. and counting the total population, respectively. Panel B presents the demographic composition of schools in the Zearn data (Column 1) and of all U.S. K-12 schools (Column 2), calculated using school-level data from the Common Core dataset as constructed by MDR Education, a private education data firm. The first three rows of Panel B show the 25th, 50th, and 75th percentiles of share of Black students in Zearn schools (Column 1) and in all US K-12 schools (Column 2). Rows 4-6 and 7-9 of Panel B replicate Rows 1-3 using the share of Hispanic students and the share of students receiving free or reduced-price lunch meals. Rows 10 and 11 of Panel B display the number of Zearn schools matched to the Common Core data and the number of students in those schools. Panel C compares the share of students by region in Zearn vs. the US population. Data sources: Zearn, ACS, Common Core.}
    {}
```

```{=latex}
\tablepdf
    {city_crosswalk} % label
    {City to County Crosswalk} % title
    {tables_cropped.pdf} % path to PDF with cropped tables
    {11} % page number in PDF
%%%%% Notes %%%%%
    {This table shows our metro area (city) to county crosswalk. We assigned metros to counties and verified that a significant portion of the county population was in the metro of interest. Some large metros share a county; in this case the smaller metro was subsumed into the larger metro.}
    {}
```

```{=latex}
\tablepdf
    {reopenings_states} % label
    {List of Re-Opening States and Control States for Event Studies} % title
    {tables_cropped.pdf} % path to PDF with cropped tables
    {12} % page number in PDF
%%%%% Notes %%%%%
    {This table lists the treatment and control states for the analysis of state reopenings in Appendix Figure \ref{fig:reopenings} and Appendix Table \ref{tab:reopening_effects}. Column (1) displays the control states in the event study of consumer spending (as measured in the Affinity data) described in Appendix Figure \ref{fig:reopenings} and Appendix Table \ref{tab:reopening_effects}. Column (2) replicates Column (1) for employment (as measured in the Paychex-Intuit data). Column (3) replicates Column (1) for the number of small businesses open (as measured in the Womply data).}
    {}
```

```{=latex}
\tablepdf
    {reopening_effects} % label
    {Causal Effects of Re-Openings on Economic Activity} % title
    {tables_cropped.pdf} % path to PDF with cropped tables
    {13} % page number in PDF
%%%%% Notes %%%%%
    {This table estimates the effects of state reopenings on various outcomes using an event study design based on states that reopened non-essential businesses between April 20 to 27, 2020. Each state that reopens is matched to multiple control states (listed in Appendix Table \ref{tab:reopenings_states}) that did not reopen within the subsequent 3 weeks but had similar trends of the outcome variable during the weeks preceding the reopening. We construct the control group separately for each re-opening day and then stack the resulting event studies to align the events. All estimates are from OLS regressions at the state x week level on an indicator variable for the state being a state that reopened, an indicator variable for the date being after the reopening date, and the interaction between these two variables. We report the coefficient and standard error on the interaction term, which we refer to as the difference-in-differences (DD) estimate of the effect of reopening. Standard errors are clustered at the state level and reported in parentheses. The dependent variable is rescaled to be in percentage terms such that, for example, the first row of Column (1) indicates that the difference-in-differences estimate for the effect of reopening on consumer spending over a two-week horizon is a {{did_beta_spend_all_twoweeks}} percentage point increase in consumer spending. The third row indicates the “Analysis Window” used in the regression: for example, the sample in column (1) is restricted to the two weeks before and after the date of reopening, whereas the sample in column (2) is restricted to the three weeks before and after the date of reopening. The last row shows the mean decline in the outcome variable across states from the period January 4 to 31, 2020 to the period March 25 to April 14, 2020 - except in Columns (1) and (2) where the reference period is January 6 to February 2, 2020. Columns (1) and (2) show the estimated effect of reopening on consumer spending using data from Affinity Solutions. Consumer spending is expressed as a percentage change relative to January 6 to February 2, 2020, and seasonally adjusted using 2019 data. Columns (3) and (4) replicate columns (1) and (2) using changes in employment as the dependent variable. Employment is calculated using Paychex-Intuit data and expressed as a percentage change relative to January 4 to 31, 2020. Columns (5) and (6) replicate columns (1) and (2) respectively using the number of small businesses open as the dependent variable, calculated using Womply data and expressed as a percentage change relative to January 4 to 31, 2020. Columns (1), (3), and (5) correspond to the specifications displayed in Appendix Figure \ref{fig:reopenings_eventstudies}. Data sources: Affinity Solutions, Paychex, Intuit, Womply.}
    {}
```
```{=latex}
\tablepdf
    {ppp_effects} % label
    {Causal Effect of the Paycheck Protection Program on Employment} % title
    {tables_cropped.pdf} % path to PDF with cropped tables
    {14} % page number in PDF
%%%%% Notes %%%%%
    {This table reports difference-in-differences (DD) estimates of the effect of PPP eligibility (defined as the parent firm having fewer than 500 employees) on employment. The outcome variable is employment at the county x 2-digit NAICS x wage quartile x PPP eligibility x week level, excluding the Accommodations and Food Services Sector (NAICS 72), expressed as a percentage change relative to January 4 to 31, 2020. Both columns present regressions in combined Paychex-Earnin data. In the baseline estimate in column (1), we begin by restricting the sample to firms with 100-799 employees. We then reweight these cells so that employment shares by industry within each eligibility group match the overall employment shares by industry in January 4 to 31, 2020. Finally, we report estimates from an OLS regression of changes in employment on county x wage quartile x week fixed effects, an indicator for PPP eligibility (firm size $<500$ employees), and an interaction term between PPP eligibility and an indicator for the date being after April 3, 2020 (the DD estimate). The sample for this regression is limited to weeks ending between March 11 and August 15, 2020. The DD estimate is the coefficient on the interaction term for PPP eligibility and the date being after April 3. We cluster standard errors (reported in parentheses) at the county x industry x eligibility level, and winsorize the dependent variable at the 99th percentile. We use reweighted employment in January 4 to 31, 2020, as regression weights. Column (2) replicates Column (1), restricting to firms with between 300 and 699 employees. Data sources: Paychex, Earnin.}
    {}
```
